ABSTRACT:This
talk covers the development of a real-time monocular visual simultaneous localization
and mapping(SLAM) algorithm and
its results for its application in the area of autonomous underwater ship hull
inspection.The goal of this work
is to automatically map and navigate he underwater surface area of a ship hull
for foreign object detection and maintenance inspection tasks.The proposed algorithm overcomes some of
the specific challenges associated with underwater visual SLAM, namely limited
field of view imagery and feature-poor regions.It does so by exploiting our SLAM
navigation prior within the image registration pipeline and by being selective
about which imagery is considered informative in terms of our visual SLAM
map.A novel online bag-of-words
measure for intra-and inter-image saliency is introduced, and is shown to be
useful for both key-frame selection and for information-gain based link
hypothesis.An online planning
framework that computes the most informative regions of the hull for visitation
in the SLAM graph, based upon visually plausible expected information-gain, is
also shown.Results from several
real-world hull inspection experiments are shown to validate the overall
approach- including one survey comprising a 3.4 hour / 2.7 km long trajectory

BIO:Ryan
M. Eustice received the B.S. degree in mechanical
engineering from Michigan State University, East Lansing, MI in 1998, and the
Ph.D. degree in ocean engineering from the Massachusetts Institute of
Technology/Woods Hole Oceanographic Institution Joint Program, Woods Hole, MA,
in 2005.Currently, he is an
Assistant Professor with the Department of Naval Architecture and Marine
Engineering, University of Michigan, Ann Arbor, with courtesy appointments in
the Department of Electrical Engineering and Computer Science, and in the
Department of Mechanical Engineering. His research interests include autonomous
navigation and mapping, computer vision and image processing, mobile robotics,
and autonomous underwater vehicles.